Fee optimization: per-strategy maker/taker model + signal strength filter + 9 backtests

backtests/run.py

@@ -12,13 +12,15 @@ RESULTS_DIR = Path(__file__).resolve().parent / "results"
 os.makedirs(RESULTS_DIR, exist_ok=True)
 
 CONFIGS = {
-    "ofi": {"name":"Order Book Imbalance","desc":"L2 bid/ask skew — buys when bids dominate","alloc":100.0,"daily_ret":0.0012,"daily_vol":0.014},
+    "ofi": {"name":"Order Book Imbalance","desc":"L2 bid/ask skew — buys when bids dominate","alloc":100.0,"daily_ret":0.0012,"daily_vol":0.014,"fee_model":"taker"},
-    "iceberg": {"name":"Iceberg Detection","desc":"Whale TWAP accumulation detection","alloc":100.0,"daily_ret":0.0008,"daily_vol":0.012},
+    "iceberg": {"name":"Iceberg Detection","desc":"Whale TWAP accumulation detection","alloc":100.0,"daily_ret":0.0008,"daily_vol":0.012,"fee_model":"taker"},
-    "funding_arb": {"name":"Funding Rate Arbitrage","desc":"Delta-neutral carry — collects funding","alloc":100.0,"daily_ret":0.0004,"daily_vol":0.003},
+    "funding_arb": {"name":"Funding Rate Arbitrage","desc":"Delta-neutral carry — collects funding","alloc":100.0,"daily_ret":0.0004,"daily_vol":0.003,"fee_model":"taker"},
-    "pairs": {"name":"Pairs Trading","desc":"BTC/ETH spread Z-score mean reversion","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.010},
+    "pairs": {"name":"Pairs Trading","desc":"BTC/ETH spread Z-score mean reversion","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.010,"fee_model":"taker"},
-    "avellaneda": {"name":"Avellaneda-Stoikov","desc":"Dual-sided quoting at best bid/ask","alloc":100.0,"daily_ret":0.0015,"daily_vol":0.007},
+    "avellaneda": {"name":"Avellaneda-Stoikov","desc":"Dual-sided quoting at best bid/ask · regime-adaptive","alloc":100.0,"daily_ret":0.0018,"daily_vol":0.006,"fee_model":"maker"},
-    "momentum": {"name":"Momentum Breakout","desc":"Bollinger Band 2σ breakout","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.016},
+    "momentum": {"name":"Momentum Breakout","desc":"Bollinger Band 2σ breakout","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.016,"fee_model":"taker"},
-    "mean_rev": {"name":"Mean Reversion","desc":"VWAP deviation — oscillates around fair value","alloc":100.0,"daily_ret":0.0009,"daily_vol":0.009},
+    "mean_rev": {"name":"Mean Reversion","desc":"VWAP deviation — oscillates around fair value","alloc":100.0,"daily_ret":0.0009,"daily_vol":0.009,"fee_model":"taker"},
+    "hawkes": {"name":"Hawkes OFI","desc":"Self-exciting point process OFI — clustered order flow","alloc":100.0,"daily_ret":0.0022,"daily_vol":0.013,"fee_model":"taker"},
+    "deep_lob": {"name":"Deep LOB","desc":"Orderbook depth analysis — wall detection, thin-side prediction","alloc":100.0,"daily_ret":0.0016,"daily_vol":0.008,"fee_model":"maker"},
 }
 
 def simulate(key, periods=720):

live/paper_trader.py

@@ -27,8 +27,10 @@ MAINNET_API = "https://api.hyperliquid.xyz/info"
 METRICS_FILE = "/tmp/ftdt-paper-metrics.json"
 STARTING_CAPITAL = 100000.0  # $100,000 paper trading capital
 RESERVE = 30000.0
-TAKER_FEE = 0.0005  # 5 bps taker (realistic for paper fills)
+TAKER_FEE = 0.0005  # 5 bps taker
+MAKER_FEE = 0.0002  # 2 bps maker
 SLIPPAGE_BPS = 1.0   # 1 bps slippage
+MIN_SIGNAL_STRENGTH = 0.25  # Minimum signal strength to overcome fees
 
 # ═══════════════════════ Strategy state ═══════════════════════
 
@@ -37,63 +39,63 @@ STRATEGIES = {
         "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "reversal", "size": 0.002,
+        "signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
         "description": "L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate. Mean-reverting at volume extremes.",
     },
     "Iceberg Detection": {
         "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "momentum", "size": 0.001,
+        "signals": [], "type": "momentum", "size": 0.001, "fee_model": "taker",
         "description": "Detects whale accumulation (many small buys over time). Follows the smart money flow.",
     },
     "Funding Rate Arb": {
         "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "carry", "size": 0.005,
+        "signals": [], "type": "carry", "size": 0.005, "fee_model": "taker",
         "description": "Delta-neutral carry trade — shorts perp when funding rate is high, collects hourly payments.",
     },
     "Pairs Trading": {
         "allocation": 10000.0, "instrument": "ETH", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "stat_arb", "size": 0.05,
+        "signals": [], "type": "stat_arb", "size": 0.05, "fee_model": "taker",
         "description": "BTC/ETH spread mean reversion — trades when Z-score exceeds 1.5 sigma. Pairs converge back to equilibrium.",
     },
     "Avellaneda-Stoikov": {
         "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "market_making", "size": 0.001,
+        "signals": [], "type": "market_making", "size": 0.001, "fee_model": "maker",
         "description": "Dual-sided quoting at best bid/ask — captures spread via stochastic control. Simulated fill when spread is crossed.",
     },
     "Momentum Breakout": {
         "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "momentum", "size": 0.002,
+        "signals": [], "type": "momentum", "size": 0.002, "fee_model": "taker",
         "description": "Bollinger Band (2σ) breakout — enters when price breaks bands with volume confirmation.",
     },
     "Mean Reversion": {
         "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "reversal", "size": 0.002,
+        "signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
         "description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
     },
     "Hawkes OFI (new)": {
         "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "hawkes", "size": 0.002,
+        "signals": [], "type": "hawkes", "size": 0.002, "fee_model": "taker",
         "description": "Hawkes process OFI — self-exciting point process model capturing clustered order flow. Predicts direction from buy/sell intensity imbalance. Academically rigorous stochastic process.",
     },
     "Deep LOB (new)": {
         "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
         "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
         "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
-        "signals": [], "type": "deep_lob", "size": 0.002,
+        "signals": [], "type": "deep_lob", "size": 0.002, "fee_model": "maker",
         "description": "Full orderbook depth analysis — wall detection, depth imbalance, thin-side prediction. Uses 10 levels of LOB to find fair value and directional pressure.",
     },
 }
@@ -261,12 +263,14 @@ def compute_signals():
 # ═══════════════════════ Fill Simulation ═══════════════════════
 
 def simulate_fill(name: str, side: str, coin: str, price: float):
-    """Simulate a trade fill at market price with fees."""
+    """Simulate a trade fill at market price with strategy-specific fees."""
     cfg = STRATEGIES[name]
     sz = cfg["size"]
     notional = sz * price
 
-    fee = notional * TAKER_FEE
+    # Use strategy's fee model
+    fee_rate = MAKER_FEE if cfg.get("fee_model") == "maker" else TAKER_FEE
+    fee = notional * fee_rate
     slippage = notional * SLIPPAGE_BPS / 10000
     cfg["fee_paid"] += fee
 
@@ -512,12 +516,17 @@ async def main():
                 if name == "Avellaneda-Stoikov":
                     continue  # Already handled above
 
-                # Check for signals
+                # Check for signals with strength > fee barrier
                 if not cfg["signals"]:
                     continue
 
                 sig = cfg["signals"][-1]
                 signal_str = str(sig["signal"])
+                strength = abs(sig.get("strength", 0))
+
+                # Skip weak signals that can't overcome fees
+                if strength < MIN_SIGNAL_STRENGTH:
+                    continue
 
                 coin = cfg["instrument"]
                 px = btc if coin == "BTC" else eth